Border points are those instances located at the outer margin of dense clusters of samples. The detection is important in many areas such as data mining, image processing, robotics, geographic information systems and pattern recognition. In this paper we propose a novel method to detect border samples. The proposed method makes use of a discretization and works on partitions of the set of points. Then the border samples are detected by applying an algorithm similar to the presented in reference [8] on the sides of convex hulls. We apply the novel algorithm on classification task of data mining; experimental results show the effectiveness of our method. © 2011 Springer-Verlag.
CITATION STYLE
López Chau, A., Li, X., Yu, W., Cervantes, J., & Mejía-Álvarez, P. (2011). Border samples detection for data mining applications using non convex hulls. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7095 LNAI, pp. 261–272). https://doi.org/10.1007/978-3-642-25330-0_23
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